Yang Shuoheng, Fei Ningbo, Li Junpeng, Li Guangsheng, Hu Yong
Spinal Division, Orthopedic and Traumatology Center, The Affiliated Hospital of Guangdong Medical University, Zhanjiang 524002, China.
Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China.
Bioengineering (Basel). 2025 Jun 28;12(7):709. doi: 10.3390/bioengineering12070709.
Diffusion Tensor Imaging (DTI) is a crucial imaging technique for accurately assessing pathological changes in Cervical Spondylotic Myelopathy (CSM). However, the segmentation of spinal cord DTI images primarily relies on manual methods, which are labor-intensive and heavily dependent on the subjective experience of clinicians, and existing research on DTI automatic segmentation cannot fully satisfy clinical requirements. Thus, this poses significant challenges for DTI-assisted diagnostic decision-making. This study aimed to deliver AI-driven segmentation for spinal cord DTI. To achieve this goal, a comparison experiment of candidate input features was conducted, with the preliminary results confirming the effectiveness of applying a diffusion-free image (B0 image) for DTI segmentation. Furthermore, a deep-learning-based model, named SCS-Net (Spinal Cord Segmentation Network), was proposed accordingly. The model applies a classical U-shaped architecture with a lightweight feature extraction module, which can effectively alleviate the training data scarcity problem. The proposed method supports eight-region spinal cord segmentation, i.e., the lateral, dorsal, ventral, and gray matter areas on the left and right sides. To evaluate this method, 89 CSM patients from a single center were collected. The model demonstrated satisfactory accuracy for both general segmentation metrics (precision, recall, and Dice coefficient) and a DTI-specific feature index. In particular, the proposed model's error rate for the DTI-specific feature index was evaluated as 5.32%, 10.14%, 7.37%, and 5.70% on the left side, and 4.60%, 9.60%, 8.74%, and 6.27% on the right side of the spinal cord, respectively, affirming the model's consistent performance for radiological rationality. In conclusion, the proposed AI-driven segmentation model significantly reduces the dependence on DTI manual interpretation, providing a feasible solution that can improve potential diagnostic outcomes for patients.
扩散张量成像（DTI）是准确评估脊髓型颈椎病（CSM）病理变化的关键成像技术。然而，脊髓DTI图像的分割主要依赖于手动方法，这种方法劳动强度大，且严重依赖临床医生的主观经验，现有的DTI自动分割研究尚不能完全满足临床需求。因此，这给DTI辅助诊断决策带来了重大挑战。本研究旨在实现基于人工智能的脊髓DTI分割。为实现这一目标，进行了候选输入特征的对比实验，初步结果证实了应用无扩散图像（B0图像）进行DTI分割的有效性。此外，相应地提出了一种基于深度学习的模型，名为SCS-Net（脊髓分割网络）。该模型采用经典的U形架构和轻量级特征提取模块，可有效缓解训练数据稀缺问题。所提出的方法支持八区域脊髓分割，即左右两侧的外侧、背侧、腹侧和灰质区域。为评估该方法，收集了来自单一中心的89例CSM患者的数据。该模型在一般分割指标（精度、召回率和骰子系数）以及DTI特定特征指标方面均表现出令人满意的准确性。特别是，所提出模型在脊髓左侧的DTI特定特征指标的错误率分别评估为5.32%、10.14%、7.37%和5.70%，在脊髓右侧分别为4.60%、9.60%、8.74%和6.27%，证实了该模型在放射学合理性方面的一致性能。总之，所提出的基于人工智能的分割模型显著降低了对DTI人工解读的依赖，提供了一种可行的解决方案，可改善患者的潜在诊断结果。
